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Traditionally, capsule wardrobes are manually designed by expert fashionistas through their creativity and technical prowess. The goal is to curate minimal fashion items that can be assembled into several compatible and versatile outfits. It is usually a cost and time intensive process, and hence lacks scalability. Although there are a few approaches that attempt to automate the process, they tend to ignore the price of items or shopping budget. In this article, we formulate this task as a multi-objective budget constrained capsule wardrobe recommendation ( MOBCCWR ) problem. It is modeled as a bipartite graph having two disjoint vertex sets corresponding to top-wear and bottom-wear items, respectively. An edge represents compatibility between the corresponding item pairs. The objective is to find a 1-neighbor subset of fashion items as a capsule wardrobe that jointly maximize compatibility and versatility scores by considering corresponding user-specified preference weight coefficients and an overall shopping budget as a means of achieving personalization. We study the complexity class of MOBCCWR , show that it is NP-Complete, and propose a greedy algorithm for finding a near-optimal solution in real time. We also analyze the time complexity and approximation bound for our algorithm. Experimental results show the effectiveness of the proposed approach on both real and synthetic datasets.

Fashionable outfits are generally created by expert fashionistas, who use their creativity and in-depth understanding of fashion to make attractive outfits. Over the past few years, automation of outfit composition has gained much attention from the research community. Most of the existing outfit recommendation systems focus on pairwise item compatibility prediction (using visual and text features) to score an outfit combination having several items, followed by recommendation of top-n outfits or a capsule wardrobe having a collection of outfits based on user’s fashion taste. However, none of these consider a user’s preference of price range for individual clothing types or an overall shopping budget for a set of items. In this article, we propose a box recommendation framework—BOXREC—which at first collects user preferences across different item types (namely, top-wear, bottom-wear, and foot-wear) including price range of each type and a maximum shopping budget for a particular shopping session. It then generates a set of preferred outfits by retrieving all types of preferred items from the database (according to user specified preferences including price ranges), creates all possible combinations of three preferred items (belonging to distinct item types), and verifies each combination using an outfit scoring framework—BOXREC-OSF. Finally, it provides a box full of fashion items, such that different combinations of the items maximize the number of outfits suitable for an occasion while satisfying maximum shopping budget. We create an extensively annotated dataset of male fashion items across various types and categories (each having associated price) and a manually annotated positive and negative formal as well as casual outfit dataset. We consider a set of recently published pairwise compatibility prediction methods as competitors of BOXREC-OSF. Empirical results show superior performance of BOXREC-OSF over the baseline methods. We found encouraging results by performing both quantitative and qualitative analysis of the recommendations produced by BOXREC. Finally, based on user feedback corresponding to the recommendations given by BOXREC, we show that disliked or unpopular items can be a part of attractive outfits.